Finance Language Model Evaluation (FLaME)
- URL: http://arxiv.org/abs/2506.15846v1
- Date: Wed, 18 Jun 2025 19:54:33 GMT
- Title: Finance Language Model Evaluation (FLaME)
- Abstract summary: Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks.<n>We present the first holistic benchmarking suite for Financial Language Model Evaluation (FLaME)<n>We are the first research paper to comprehensively study LMs against'reasoning-reinforced' LMs.
- Score: 5.904572835181286
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensive tasks in finance remains difficult to assess due to major gaps in the methodologies of existing evaluation frameworks, which have caused an erroneous belief in a far lower bound of LMs' performance on common Finance NLP (FinNLP) tasks. To demonstrate the potential of LMs for these FinNLP tasks, we present the first holistic benchmarking suite for Financial Language Model Evaluation (FLaME). We are the first research paper to comprehensively study LMs against 'reasoning-reinforced' LMs, with an empirical study of 23 foundation LMs over 20 core NLP tasks in finance. We open-source our framework software along with all data and results.
Related papers
- SAHM: A Benchmark for Arabic Financial and Shari'ah-Compliant Reasoning [59.20983102634086]
We introduce SAHM, a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari'ah-compliant reasoning.<n> SAHM contains 14,380 expert-verified instances spanning seven tasks: AAOIFI standards QA, fatwa-based QA/MCQ, accounting and business exams, financial sentiment analysis, extractive summarization, and event-cause reasoning.<n>We find that Arabic fluency does not reliably translate to evidence-grounded financial reasoning.
arXiv Detail & Related papers (2026-04-21T05:24:08Z) - The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems [54.12165004393043]
FinMMEval 2026 offers three interconnected tasks that span financial understanding, reasoning, and decision-making.<n>The lab aims to promote the development of robust, transparent, and globally inclusive financial AI systems.
arXiv Detail & Related papers (2026-02-11T14:14:06Z) - The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications [4.211847212372977]
The LLM Pro Finance Suite is a collection of five instruction-tuned large language models (LLMs) specifically designed for financial applications.<n>Our approach focuses on enhancing generalist instruction-tuned models, leveraging their existing strengths in instruction following, reasoning, and toxicity control.<n>We evaluate the Suite on a comprehensive financial benchmark suite, demonstrating consistent improvement over state-of-the-art baselines in finance-oriented tasks and financial translation.
arXiv Detail & Related papers (2025-11-07T11:08:31Z) - FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain [54.06289302468199]
FinTrust is a benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications.<n> proprietary models like o4-mini outperforms in most tasks such as safety.<n>Open-source models like DeepSeek-V3 have advantage in specific areas like industry-level fairness.
arXiv Detail & Related papers (2025-10-17T01:45:49Z) - FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs [15.230256296815565]
FinMaster is a benchmark designed to assess the capabilities of large language models (LLMs) in financial literacy, accounting, auditing, and consulting.<n>FinMaster comprises three main modules: FinSim, FinSuite, and FinEval.<n>Experiments reveal critical capability gaps in financial reasoning, with accuracy dropping from over 90% on basic tasks to merely 37% on complex scenarios.
arXiv Detail & Related papers (2025-05-18T11:47:55Z) - Bridging Language Models and Financial Analysis [49.361943182322385]
The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing.<n>Financial data is often embedded in intricate relationships across textual content, numerical tables, and visual charts.<n>Despite the fast pace of innovation in LLM research, there remains a significant gap in their practical adoption within the finance industry.
arXiv Detail & Related papers (2025-03-14T01:35:20Z) - Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models [22.594428755214356]
"Golden Touchstone" is the first comprehensive bilingual benchmark for financial LLMs.
benchmarks include a variety of financial tasks aimed at thoroughly assessing models' language understanding and generation capabilities.
We open-sourced Touchstone-GPT, a financial LLM trained through continual pre-training and financial instruction tuning.
arXiv Detail & Related papers (2024-11-09T20:09:11Z) - Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications [88.96861155804935]
We introduce textitOpen-FinLLMs, the first open-source multimodal financial LLMs.<n>FinLLaMA is pre-trained on a comprehensive 52-billion-token corpus; FinLLaMA-Instruct, fine-tuned with 573K financial instructions; and FinLLaVA, enhanced with 1.43M multimodal tuning pairs.<n>We evaluate Open-FinLLMs across 14 financial tasks, 30 datasets, and 4 multimodal tasks in zero-shot, few-shot, and supervised fine-tuning settings.
arXiv Detail & Related papers (2024-08-20T16:15:28Z) - CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models [61.324062412648075]
CFinBench is an evaluation benchmark for assessing the financial knowledge of large language models (LLMs) under Chinese context.
It comprises 99,100 questions spanning 43 second-level categories with 3 question types: single-choice, multiple-choice and judgment.
The results show that GPT4 and some Chinese-oriented models lead the benchmark, with the highest average accuracy being 60.16%.
arXiv Detail & Related papers (2024-07-02T14:34:36Z) - FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks.
FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
arXiv Detail & Related papers (2024-02-20T02:16:16Z) - D\'olares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs
Between Spanish and English [67.48541936784501]
Tois'on de Oro is the first framework that establishes instruction datasets, finetuned LLMs, and evaluation benchmark for financial LLMs in Spanish joint with English.
We construct a rigorously curated bilingual instruction dataset including over 144K Spanish and English samples from 15 datasets covering 7 tasks.
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
arXiv Detail & Related papers (2024-02-12T04:50:31Z) - A Survey of Large Language Models in Finance (FinLLMs) [10.195778659105626]
Large Language Models (LLMs) have shown remarkable capabilities across a wide variety of Natural Language Processing (NLP) tasks.
This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges.
To support AI research in finance, we compile a collection of accessible datasets and evaluation benchmarks on GitHub.
arXiv Detail & Related papers (2024-02-04T02:06:57Z) - Revolutionizing Finance with LLMs: An Overview of Applications and Insights [45.660896719456886]
Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields.<n>These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice.
arXiv Detail & Related papers (2024-01-22T01:06:17Z) - Is ChatGPT a Financial Expert? Evaluating Language Models on Financial
Natural Language Processing [22.754757518792395]
FinLMEval is a framework for Financial Language Model Evaluation.
This study compares the performance of encoder-only language models and the decoder-only language models.
arXiv Detail & Related papers (2023-10-19T11:43:15Z) - PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark
for Finance [63.51545277822702]
PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data.
We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks.
We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
arXiv Detail & Related papers (2023-06-08T14:20:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.